Hi Greg,
The problem is that I also have restrictions for each variable (they must be
higher than -.07 and smaller than .2) and I'm dealing with a lot of them.
I've already tried the second approach but, as far as it seems, the function
doesn't satisfy my objective.
That's what I'm doing:
.....................
faum = function(aum)
{
(...)
ifelse(colMeans(prob) >.65, totm <- (sum(marg)), totm <- -1e10)
#prob is a transformation of 'aum' and sum(marg) (always positive) is what I
want to maximize
(...)
return(-totm)
}
optim(rep(0,nrow), faum, method="L-BFGS-B", control=list(trace=12),
lower=rep(-.07,nrow), upper=rep(.2,nrow) )
.....................
The function gives me 1e10 if my initial values for the parameters doesn't
satisfy mean>.65 and doesn't improve my initial conditions if they satisfy
mean<=.65.
I know that a solution with mean>.65 exists, I can find it by hand.
--
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